A Comprehensive Evaluation of Supervised Machine Learning for the Phase Identification Problem

Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification. Keywords—Distribution network, machine learning, network topology, phase identification, smart grid.

[1]  Jouko Lampinen,et al.  Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.

[2]  Houman Pezeshki,et al.  Consumer phase identification in a three phase unbalanced LV distribution network , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[3]  Y. Borgne.  Bias-Variance trade-off characterization in a classification problem What differences with regression ? , 2005 .

[4]  W. Wang,et al.  Advanced Metering Infrastructure Data Driven Phase Identification in Smart Grid , 2017 .

[5]  Sean Gerrish,et al.  Black Box Variational Inference , 2013, AISTATS.

[6]  Andreas Krause,et al.  Submodular Function Maximization , 2014, Tractability.

[7]  Wei-Yin Loh,et al.  Classification and Regression Tree Methods , 2008 .

[8]  Sepp Hochreiter,et al.  Self-Normalizing Neural Networks , 2017, NIPS.

[9]  B. Roe,et al.  Boosted decision trees as an alternative to artificial neural networks for particle identification , 2004, physics/0408124.

[10]  Vijay Arya,et al.  Phase identification in smart grids , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[11]  Kameshwar Poolla,et al.  Phase identification in distribution networks with micro-synchrophasors , 2015, 2015 IEEE Power & Energy Society General Meeting.

[12]  Juan Li,et al.  Phase Identification in Electric Power Distribution Systems by Clustering of Smart Meter Data , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[13]  Yasin Abbasi-Yadkori,et al.  Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph , 2011, IJCAI.

[14]  Hui Lin,et al.  How to select a good training-data subset for transcription: submodular active selection for sequences , 2009, INTERSPEECH.

[15]  C. S. Chen,et al.  Design of Phase Identification System to Support Three-Phase Loading Balance of Distribution Feeders , 2011, IEEE Transactions on Industry Applications.

[16]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[17]  Robert P. Broadwater,et al.  Phase prediction in distribution systems , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[18]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[19]  Noboru Murata,et al.  Neural Network with Unbounded Activation Functions is Universal Approximator , 2015, 1505.03654.

[20]  David M. Blei,et al.  Variational Inference: A Review for Statisticians , 2016, ArXiv.

[21]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[22]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[23]  Tom A. Short,et al.  Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling , 2013, IEEE Transactions on Smart Grid.